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Assessing Improvement Opportunities and Risks of Supply Chain Transformation Projects

Alessandro Brun and Maria Caridi† Department of Management, Economics and Industrial Engineering,

Politecnico di Milano, Milan Italy

1. Introduction

Planning and control systems have deeply evolved in recent years in order to cope with the needs of manufacturing firms. It is possible to identify a route of evolution that begins with the introduction of MRP systems (Orlicky, 1975) and, passing through the management of capacity and materials constraints, moves towards contemporary APS (Advanced Planning & Scheduling) and SCM (Supply Chain Management) solutions. New functions, such as ATP (Available to Promise) or CTP (Capable to Promise), are nowadays considered necessary conditions for order planning and quoting. On the other hand, the offer of planning systems has reached a high level of performance with APS, where huge sets of objectives and constraints are standardised in libraries so that manufacturing systems can be modelled in detail. APS/SCM systems represent the most relevant innovation in the world of manufacturing since the introduction of MRP systems in the Seventies (Turbide, 1998). In fact they marry the potentialities of modern processing systems with the most sophisticated heuristic / optimising / AI-based techniques developed by operations research. Although most of the benefits provided by APS/SCM systems are generally quite apparent to operations managers which have to manage complex logistics system, a fair evaluation of these benefits should be provided by APS/SCM Vendors in order to prove that the huge amount of investment connected to the acquisition, implementation and maintenance of APS/SCM is paid back. In particular, the evaluation process could be divided into two different phases: the first one concerns the quantification of the expected improvement, while the latter focuses on the risks which could turn out in lower-than-expected returns. It is worth here specifying that the term “risk” could be intended not only to address negative cases (actual benefits lower than expected) but even positive cases (actual benefits higher than expected); moreover, when evaluating the project risk, intangible benefits (and drawbacks), such as the organisational impact of the IT project, should be considered. This work describes the achievements of a research project, carried out at Politecnico di Milano, whose objective is to develop a new methodology, SNOpAck (Supply Network

† Corresponding author (e-mail: [email protected]).

Source: Supply Chain,Theory and Applications, Book edited by: Vedran Kordic, ISBN 978-3-902613-22-6, pp. 558, February 2008, I-Tech Education and Publishing, Vienna, Austria

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Opportunity Assessment Package), for the value assessment of APS/SCM system application in a supply chain. The chapter is arranged as follows. Section 2 presents a brief literature review of value assessment approaches. Section 3 introduces a new methodology, which focuses on the value assessment of APS/SCM information systems. Section 4 presents a case study focused on the first 3 steps of the methodology (namely, 1. Preliminary analysis, 2. Analysis of operations and business processes and of Key Performance Indicators and 3. Evaluation of the APS/SCM solution). Section 5 reports some concluding remarks and suggests future research paths.

2. Theoretical framework

In recent years many studies have been focused on the evaluation of the possible benefits

and costs related to the implementation of an information system into a company. Section

2.1 presents a survey of the most interesting contributions dealing with the value assessment

of information system (IS) projects, whereas section 2.2 focuses on the project carried out at

Politecnico di Milano, by highlighting its main features and goals.

2.1 Methodologies for the value assessment of IS projects

In the last three decades IS implementation has been one of the most important issues for

the management of almost all kinds of companies. Several empirical studies have shown

that organisations are not at all comfortable in the evaluation of IS investments (Willcocks &

Lester, 1993). A large number of methodologies and techniques has been therefore proposed

to help in the evaluation of IS investments. Different researchers could identify (Renkema &

Berghout, 1997) over 65 methods supporting the evaluation of IS investments. Actually too

many methods exist, “roughly one per consultant” (Farbey & Finkelstein, 2000), but most of

them are not published from consultancy firms because of the possible loss of competitive

advantage.

Several survey papers have shown that most methods of information system evaluation

used in the practice, both ex-ante and ex-post, are variants of consolidated techniques and

ways of thinking, which can be traced back to the following classification proposed by the

works of Farbey et al. (1993) and Farbey & Finkelstein (2000): i. quantitative and comparative

methods (or “objective” methods), provide a quantification of costs and benefits in economic

terms, so allowing to compare the costs and benefits of different information systems; such

methods usually rely on conventional accounting methods; ii. qualitative and exploratory

methods (or “subjective” methods) emphasise the importance of understanding the

opportunities as well as the threats which the change may bring to some stakeholders, with

the aim of obtaining an agreement on the objectives through a process of exploration and

mutual learning.

The classification framework proposed by Farbey et al. (1993) and Farbey & Finkelstein

(2000) is reported in Tables 1 and 2.

Notice that, in spite of the wide availability of value assessment approaches, most

companies apply simple accounting techniques, belonging to the cluster of quantitative and

comparative methods: Ballantine & Stray (1999) carried out a survey showing that the most

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used methods for the evaluation of IS projects in companies are still ROI and Cost-Benefit

Analysis methods.

Finally, it is worth highlighting that researches validating evaluation methods are hardly

available and that general prescriptions about the use of which method in which

circumstances can not be given (Renkema & Berghout, 1997).

2.2 Value assessment of APS / SCM projects

When dealing with the introduction of information systems for Supply Chain Management

in a company, the topic of identifying and analysing the extent of change and of the

expected benefits (value assessment) is a key issue and no universally accepted

methodology can be found in literature, although the task of evaluating the benefits appear

simpler in this case, since the benefits are restricted to Operations.

The proposed methodology supports both industrial users during the process of “ex-ante”

evaluation of the opportunity to implement an APS/SCM solution and consulting firms

during the process of definition of the features to which address a possible choice of a

specific information system solution. The main goals driving its development are

completeness, objectiveness and possibility of a partial automation. It has resulted an

analytical methodology that, recalling the classification by Farbey et al. (1993) and Farbey &

Finkelstein (2000) (see Table 1), can be classified in the group of “cost-benefit analysis”

methodologies although it has some distinguishing features that will be deeply presented in

the following section.

3. THE SNOpAck methodology

At Politecnico di Milano a research project was carried out with the aim of developing an

original value and risk assessment methodology, called SNOpAck (Supply Network

Opportunity Assessment Package), for evaluating APS/SCM implementation projects.

When dealing with an implementation project in a specific company, the methodology aims

at answering to the following three main questions:

i. which information requirements should be addressed in order to improve company’s operations?

ii. which benefits would arise by fairly covering such requirements? iii. which is the Value (in terms of quantifiable benefits and costs) related to a specific

APS/SCM solution? An overview of the steps of the SNOpAck methodology is presented in Figure 1; each step

will be described in the following sections; further details are reported in Fahmy Salama

(2002).

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Features

Focus on cost savings

and cost displacement

Ex ante and ex post ;

future uncertainty is

considered; middle to

high cost Ex ante or ex post ; cost-

effective solutions;

“external” and “soft”

costs and benefits;

numbers more important

than process; high cost

Ex post; no cause and

effect relations can be

postulated; utilisation of

a formula; cheap

Ex ante or ex post ;

supporting

benchmarking analysis;

cheap

All options are

comprehensively dealt

with; rather complex

Data

Cost accounting and

work-study method

Tangible; direct; objective

Cost and benefit

elements expressed in a

standard money value

form; pseudo-objective

Accounting totals (e.g.

total revenue, total

labour cost)

Ratios of aggregated

numbers (e.g. IT expense

per employee)

Ranking and rating of

objectives, both tangible

and intangible

Process management

Accounting and costing staff

Calculation by professionals;

tangible costs and benefits

aggregated as cash flows

Bottom up; carried out by

experts; money values for

decision makers by

incorporating surrogate

measures

Calculation by professionals;

manipulates accounting

figures to produce a residue –

value added by management

Top-down; senior

stakeholders involved;

calculation by professionals

Many stakeholders involved;

detailed analysis required

Detail

Very high

High

High

Low

Low; aggregate

Usually very

high

Method

Cost/ revenue analysis

Return on investment

(ROI)

Cost-benefit analysis

Return on management

(ROM)

Boundary values and

spending ratios

IE, information

economics

Table 1 – Quantitative and comparative methods (Source: adapted from Farbey et al. (1993) and Farbey and Finkelstein (2000)

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Features

Ex ante; good for extracting

software requirements;

process is more important

than numbers; selection of (a)

preferred set of design goals,

(b) best design alternative;

high cost

Ex ante; iterative; incremental;

focus on added value than on

saved cost; process is more

important than numbers; high

cost

Ex ante; highly selective

Ex ante

Data

Priorities are stated by

stakeholders; subjective

evaluations of

intangibles

Indirect; subjective

evaluations of

intangibles; utility

scores

Interview or self-

expression; Quick but

consuming senior

management time

Exploratory;

uncertainty reduction

Process management

Top-down; consensus

seeking; all

stakeholders involved;

best choice is computed

Iterative; senior to

middle management

involved; variables

identified by means of

Delphi method

Senior management

define CSFs

Management scientists

working with

stakeholders

Detail

Any level

Any level;

generally

detailed

Short list of

factors

From

detailed to

abstract

Method

MOMC, multi-objective,

multi-criteria

Value analysis

Critical success factors

Experimental methods

Table 2 – Qualitative and exploratory methods (Source: adapted from Farbey et al. (1993) and Farbey and Finkelstein (2000).

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Figure 1. Structure of the SNOpAck methodology

3.1 Step 1: preliminary analysis

In the first phase, after a preliminary analysis of the organisation, an information

requirements analysis is carried out. Through a structured questionnaire, a weight is

associated to each information requirement, so to classify each of them in a range from

“irrelevant” to “highly relevant”. In order to counterbalance the subjectivity of the

company’s interviewee, the weights are corrected by identifying the supply chain typology

that best suits the observed company. In particular, adapting the work of Fisher (1997), three

main typologies have been identified, as depicted in Table 3: “efficient” supply chains for

“functional” products, “quick” (or agile) supply chains for “innovative” products and

“flexible” supply chains for “complex” products. The observed company can present a

mixture of the above stated typologies; once the specific supply chain typology is identified,

the weights are corrected by taking into account the typical pattern of information

requirements which characterise that supply chain typology.

Once the information requirements analysis has been carried out, the most relevant

requirements are selected by referring to a threshold value of the weights. For each of them,

a set of activities supported by APS/SCM systems and fulfilling the information

requirements are defined: these activities are “relevant”, in that their execution has a

considerable impact on supply chain performance.

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3.2 Step 2: Analysis of operations, business processes and Key Performance Indicators

The aim of this phase is the identification of company’s performances improvement due to

the implementation of the APS/SCM system. In order to carry out this step, a set of Key

Performance Indicators (KPIs) has been identified and, later on, an “activities-performances

relationships matrix” and a structured approach for KPI improvement evaluation have been

developed.

As far as the KPIs are concerned, the performances considered in this methodology to

evaluate the impact of APS/SCM solutions on organisations are based on a survey of the

dashboards employed to measure the effectiveness and efficiency of logistic-production

systems found out in literature, e.g. the metrics proposed by Bowersox & Closs (1996),

Stadtler & Kilger (2000) and in the SCOR model (Supply Chain Council, 2003). The resulting

KPIs can be classified in three main groups:

i. effectiveness performances, which address performances actually perceived by customers (e.g. on-time deliveries, delivery lead time);

ii. efficiency performances, which address performances not directly perceived by the customers (e.g. stock levels, work in process, resources saturation);

iii. automation performances, which address the improvement in efficiency due to the automatic execution of formerly manual activities (e.g. order entry, order release).

Moreover, by observing that in many cases a performance improvement leads to an indirect

improvement of other performances, a cause-effect relationships network linking the KPIs

has been developed. An example of relationships network is provided in Figure 2.

Source Performances

Sink Performances

DFA Demand Forecast Accuracy

ST Stockout

WIP Work in Process (WIP)

OLT Order Lead Time

Intermediate Level Performances

Dependency Relationships

SS Safety Stock

SS DFAST

OLTWIP

Figure 2. Example of relationships network.

For any of the activities identified in the previous step, the “activities-performances

relationships matrix” supports the identification of KPIs affected by a streamlining of the

activity itself, thus allowing a rapid definition of the “relevant” KPIs for the analysis and

assessment of benefits. Figure 3 depicts the process of identifying the critical KPIs starting

from the weighted information requirements.

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SC typology

Efficient Quick Flexible

Products features

BOM complexity Low Low High

Lifecycle duration

> 2 years 3 months –

1 year > 2 years

Contribution margin

1 % – 15 % > 50 % > 10 %

Product variety (variants per

category) Low (10-50) High (>300) High (>300)

Average forecasting

accuracy (error) <10 % > 40 % -

Average stock-out level

1 % - 3 % > 10 % -

Average discount at

lifecycle end (as percentage of the

price)

0 % 10 % - 30 % -

SC features

Main goal Cost efficiency

Demand is satisfied efficiently, by

minimising stock-out, discounted selling and

stock obsolescence

Timeliness in demand fulfilment

Manufacturing focus

Keeping high the manufacturing

equipment utilisation rate

Keeping some excess of manufacturing

capacity

Maximising operative flexibility

Lead-time focus Light reduction

strategy

Aggressive reduction strategy with big

investments

Aggressive reduction strategy by means of

big investments

Integration level High both with upstream and

downstream partners

High both with upstream and

downstream partners

High with upstream partners

Vendor selection approach

Selected by cost and quality

Selected by speed, flexibility, quality

Selected by speed, flexibility, quality

Inventory strategy

Keeping a high rotation rate and

minimising inventory along the SC

Minimising inventory though avoiding stock-out in new

products launch phase

-

Table 3 –Supply Chains typologies (Source: adapted from Fisher (1997)).

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Relevant Element

Requirement

Weight

PERFORMANCES

INF

OR

MA

TIO

N R

EQ

UIR

EM

EN

TS

Discrimination

threshold value

AC

TIV

ITIE

S

Activities - performances

relationships matrix

Figure 3. Tool for rapid selection of relevant activities and performances.

Finally, the structured approach for KPI improvement evaluation supports the assessment

of KPIs improvement by considering the following elements:

i. the actual widening of KPI value improvement (“performance gap”); ii. which factors determine the performance gap (“cause factors”, e.g. supplier delays,

unreliable production plan), if the gap exists. When applying the structured approach, a company’s manager is to support the

identification of the previous elements. Then, for each KPI, an analysis is carried out (jointly

with the company’s manager) with a twofold aim:

i. a weight of influence on the performance gap is assessed for each cause factor and for each influencing performance (recall Figure 2); the weights sum is 100%;

ii. the percentage reduction of each cause factor due to the adequate support of the “relevant” activities is esteemed

The overall percentage reduction of the performance gap is then calculated as a composition

of the cause factor reductions and of the cause performance improvements (cause

performance improvements have been previously calculated by means of the same

structured approach). Figure 4 depicts the structured approach as a whole.

When it is possible, besides the performance gap analysis, quantitative analysis methods can

be applied to determine the performance improvement (e.g. resource saturation).

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Actual Value:

Best or Ideal Value:

WEIGHT [%]

of FACTOR

or PERFORMANCE

IMPROVEMENT [%]

of FACTOR

or PERFORMANCE

GAP REDUCTION [%]

ACTIVITIES

SUPPORTED BY

APS / SCM

SYSTEMS

PERFORMANCE GAP

GAP CAUSE

FACTORS

CAUSE

PERFORMANCES

Gap

Value:

F a c t o r s P e r f o r m a n c e s

A c t i v i t y

Figure 4. Performance gap analysis.

3.3 Step 3: Evaluation of the APS/SCM solution

In the third step, the final assessment of the introduction of an APS/SCM solution is carried

out, by quantifying the APS/SCM benefits (Figure 5). A performance improvement usually

implies a measurable economic gain in the short term, due to an improvement of supply

chain efficiency or effectiveness or to a cost reduction for the automatic execution of former

manual activities.

Besides the short-term quantitative benefits, possible intangible benefits may arise from the

implementation of an APS/SCM system. For instance, these benefits may be related to an

improvement of the competitive advantage (e.g. an improvement in customer order

timeliness has an impact on customer service level), or to the organisational impact of the

system (e.g. an APS/SCM project usually implies a redesign of tasks and roles or even a

change management). Although it is hard to define the economic gain for the improvement

of intangible performances, it is important to check their improvement with the overall

business strategy for the supply chain management, when considering the opportunity of

implementing an APS/SCM information system solution. This topic is the object of the

following section.

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Performance: Saturation of production resources

Reduction of stock holding costs [euro]

Euro

Costs reduce thanks to smaller lot-sizing

Bottleneck cost saving per hour [euro / h]

The availability of an hour of the bottleneck allows the reduction of overtime or outsourcing

Additional margin [euro / part]

Total

According to the way the manager chooses to utilise the esteemed KPI improvement,

the economic benefit can be measured as:

Revenues increase in case of additional production and sales

Figure 5. Benefits evaluation.

3.4 Step 4: Risk analysis

Once the expected tangible benefits related to the implementation of an APS/SCM solution

have been evaluated, a further analysis is carried out, taking into consideration risk and

intangible aspects; the analysis methodology has been developed on the basis of cognitive

psychology (Kahnemann et al., 1982). In particular, the aim of the analysis is threefold:

i. to determine the probability associated to each possible project outcome; ii. to estimate the transient duration before the benefits are gained; iii. to complement the quantitative analysis with a comprehensive set of qualitative

considerations (the so called strategic issues). An interesting side result of the proposed risk analysis is the evaluation of manager’s own

risk attitude, which helps in comparing different APS/SCM projects whose outcomes

present different discrete distributions. Moreover, the risk analysis determines a ranking of

the project risks, according to their impact on project results; this information is extremely

important since it supports a focused monitoring of the risk factors which may threaten the

project’s success.

A case study presenting in detail the functioning of the Risk analysis is presented in Brun et

al. (2006).

4. Rigamari case study

This section presents an in-depth case study of application of the SNOpAck methodology to

a mechanical company, Rigamari (albeit being a real company, the company name has been

disguised). Once analysed the defects (in terms both of inefficiencies and ineffectiveness) of

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Rigamari supply chain planning process, SNOpAck methodology allowed to assess the

value of the implementation of a APS system for supporting gas turbine production. After a

detailed description of the company and, mainly, of the difficulties its supply chain suffered,

this section reports the application of SNOpAck methodology.

4.1 Company presentation

When it was established, in 1842, Rigamari was a small Italian entrepreneurial metal alloy foundry, which entered in the mechanical production in the first few years of 20th century. In 1994 the company was acquired by an US-based multinational company. The core business of the company concerns the production of compressors, gas and steam turbines for oil and chemical plants, pumps and compression facilities, gas valves and gauges, petrol pumps, control systems for looms. Production activities take place in one of the 7 Italian plants of Rigamari among which, the most prominent are those based in Florence and in Borgo Ricco. Borgo Ricco plant encompasses overall 80,000 square meters: 65,000 m2 dedicated to

machine and assembly operations of 3 different product lines: blades for steam and gas

turbines, gas gauges and fuel pumps. Overall, 130 workers are employed in the production

of blades for gas and steam turbines: in the last 2 years turnover for this product line has

more than doubled, reaching 56 Million Euro. Once completed, blades are sent to the main

Florence plant, where they are then assembled, in order to build the final machine. Besides,

within the Florence plant are located the company offices (sales, R&D, etc.). Within Borgo

Ricco plant, gas turbines accounts for the 80% of blades production, while steam turbines

production (and, on turn, production of blades for steam turbines) accounts for the

remaining 20% of orders. The case study will focus on gas turbine production.

A gas turbine employs two different kinds of blades: one for the compression stage and

another for the turbine stage – in the latter stage, blades are hit by exhaust gas with an

extremely high energy content. Both blades for the compression stage and the turbine stage

are standardized, then different turbines normally adopts blades with the same

characteristics.

Blades for gas turbines are obtained by machining operations on a die-cast piece produced

by an external supplier. Work-cycle encompasses rectification, thermal treatments (realized

by sub-suppliers) and a plethora of severe quality controls and checks (both during or after

operations).

Components of a gas turbines are divided into two groups: i. “critical” components, having

a long production (or supply) lead time, are manufactured (or supplied) on the basis of

forecasts; ii. “non-critical” components are made to order. About the 100% of components

realized within the Borgo Ricco plant are classified as critical.

The short term planning of blades production activities are derived from the mid-term

planning of gas turbines, directly managed by Florence headquarter. The chief commercial

manager is in charge of deploying a sales budget, based on historical data and forecasts. The

Master Production Schedule is based on the sales budget and spans over a 12-month period.

Once the MPS has been determined at Florence, the headquarter communicates to Borgo Ricco components requirements according to MPS and an additional set of forecasts spanning over the time period not covered by the MPS.

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Technically, the workload at Borgo Ricco is managed according to advanced order logic: that

is, components are manufactured before the actual purchase order is issued by a customer.

Theoretically speaking, the period covered by the MPS would be long enough to cover the

information requirements at Borgo Ricco, since the overall lead time at Borgo Ricco is 10

months, on average. Nonetheless, requirements issued by Florence plant are not definitive:

Commercial Officers in Florence revise sales budget every month (and components delivery

dates are changed accordingly). As it could easily be guessed, this is a bit of a problem for

Borgo Ricco planners, especially when delivery dates are anticipated. In such situations,

production activities are rescheduled manually, since planners do not have any information

tool supporting urgent order scheduling (such as a “capable-to-promise” tool).

The die-cast for the turbine section blades are ordered by Florence; as a consequence, after

receiving the requirement for a set of blades, Borgo Ricco plant should also receive the die-

casts.

As for the compression section, raw materials are ordered by Borgo Ricco: the production

manager checks the availability of raw materials, and then communicates the net

requirements to the purchasing department. Raw materials suppliers are divided in two

groups according to the kind of supply relationship with Rigamari: i. transactional

relationship, that is an arm’s length relationship wherein each purchase is considered as one

of a series of independent deals, and delivery conditions and purchase price are re-

negotiated at every single deal; ii. long-term agreements, in this case an agreement is signed

up between the two parts, so that on the one hand Rigamari undertakes to purchase a

certain volume for the next 12 months, on the other hand the supplier undertakes to deliver

goods within reduced supply lead times.

Borgo Ricco plant operates with “zero inventory”: machining operations can start only after

the arrival of raw materials for gas turbines blades. Unfortunately, delivery timeliness is by

far smaller than 100%. Nevertheless, due to the “zero inventory” objective, the make-to-

order logic theoretically eliminates obsolete stock; moreover the inventory levels of

consumables and spare parts is not relevant.

Each production line is basically dedicated to the production of a specific kind of blade.

Production planning and control is carried out by the planning office, encompassing 6

workers, along with the shop-floor responsible. In particular, the shop-floor manager is in

charge of short term production scheduling, taking a series of decisions based on past

experience, aimed at maximizing resource utilization (mainly by minimizing set-up times; in

fact the direct variable cost of scheduled resources has a small impact upon overall variable

cost of the end product). Such scheduling activities are supported by a spreadsheet with

manual data-entry.

Urgent orders, mainly due to last-minute modification in product’s technical specifications

decided in Florence, account for a 60% of total orders and invalidate schedule effectiveness.

The issuing of warning signals (such as in the case of the break-down of machines, urgent orders, etc.) is carried out in an informal way and it is not automated, since the shop floor and planning office are close to one another. When interviewed, planners stressed the lack of a more ‘active’ management of such exception signals: they would welcome, for instance, the possibility to simulate alternative scenarios in order to briskly identify the best possible course of action.

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On average, overtime accounts for one hour per day per worker on the shop floor; this is

anyway not enough, and Borgo Ricco must often rely on production capacity of sub-

suppliers (even though the Borgo Ricco plant has the technological capability to carry out

the work) to carry out the required workload. As stated by the planning department

manager “I’d rather hire another 20 guys; still, without those folks, there is yet another way

to meet Florence requirements: to set-up an adequate information system”. Both the amount

of investments and hired personnel at Rigamari must abide the strict regulations

determined by the holding company board.

Quality control activities are also manually planned and predictive maintenance is not

considered relevant. Quality control stations are considered as a part of the production

system and do not have particular planning criticality. Product quality is regarded as a

Critical Success Factor at Rigamari. That’s the reason why new products are always 100%

tested, while sampling acceptation is only carried out in case of products with a significant

reliability history. Such an effort in terms of quality control is necessary, due to the high

costs of external failures (a broken blade would mean stopping the turbine and, in turn, a

very high hourly loss for Rigamari customer). In the last few years, quality levels (mainly

measured by the level of external failures quality costs) reached by Borgo Ricco have

steadily been more than satisfactory and there is no intention to spend any additional effort

to improve the planning of quality control activities.

Some of the phases of the blades production cycle (as, for instance, thermal treatments) are

executed by sub-suppliers. The information exchange between Rigamari and sub-suppliers

(in particular in terms of visibility on production advancement at the sub-suppliers

premises) takes place on a completely informal base. The same holds true for the suppliers:

visibility on supplier processes is particularly limited when the purchase order for rough

pieces is issued by Florence.

Suppliers and sub-suppliers expediting and production advancement control are carried out

by 4 employees at the Borgo Ricco plant plus an additional (external) person, by means of

telephone or fax reminders. An increased visibility over external production would

therefore be very welcome.

Once production is terminated, finished blades are immediately sent to Florence plant.

Basically, Rigamari outsources most of its transportation activities to third-party carriers.

The portion of transports managed internally (i.e. with Rigamari’s own fleet) is not critical at

all, since there is only one single destination (from Borgo Ricco to Florence, and back) and

departures are scheduled on a daily basis; besides, transportation costs are not so significant

and truckloads are always 100% full.

Supply Chain performance is controlled directly by Florence officers; in particular, after the

delivery of a machine to the end customer, several logistics KPIs are calculated on an ex-post

basis.

4.2 An overview of Borgo Ricco plant problems

In the last 6 months, production levels have more than doubled (they have started working

on a 24/7 basis - 3 daily shifts, 7 days per week) and sub-suppliers workload has increased

accordingly, accounting for 50% of the overall production. In this situation, Borgo Ricco

situation has become unbearable.

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One of the major problems of Borgo Ricco production planning and control system is related

to timeliness and punctuality of raw materials delivery for both turbine and compression

sections. Raw materials suppliers are large steel manufacturers, having great negotiation

strength; by basing their production on long production campaigns, they often change their

supply lead times with very short notice (being such a small customer, Rigamari cannot

argue about that).

Moreover, for compression section blades, there is also the need to closely control the

external production capacity, with special regards to the first few production phases.

Performance of turbine section blades production is affected by the late delivery of rough

pieces from Florence, often later than the planned completion date of finished blades.

Another problem is low production capacity of suppliers in charge of executing either

complex or highly specialized processes: planning such suppliers production on the basis of

reliable forecasts would very important to Rigamari.

Production planning is also disturbed by frequent requests from Florence to accelerate

deliveries. Such requests are driven by the quarterly financial goals declared to

stakeholders: not to run the risk to under-perform, managers at Florence headquarter strive

to anticipate end-of-quarter deliveries in order to remain on target.

Since there is no possibility to protect the production systems against exogenous variations

and disturbance with safety stock (as stated before, the holding company requires to work

with “zero inventory”), the only source of flexibility is sub-suppliers production capacity

(which, as a matter of fact, is systematically used by Rigamari). In order to rely on such

source of flexibility, Borgo Ricco has to take into account both sub-suppliers’ lead times and

production capacity constraints. Each month, Rigamari issues an order for generic

production capacity (without specifying the exact use) – it is a sort of “advanced booking” of

production capacity. In order to book the right amount of production capacity, at the right

time, Rigamari has to forecast correctly production requirements (over a one-month time

period) and at the same time to time-phase requirements and available capacity in order to

utilize booked capacity in the best possible way (i.e. both in an efficient and effective way).

4. 3 SNOpAck methodology application

The preliminary analysis of the company was carried out by means of informal interview

with plant manager and production manager, and was focused on the evaluation of an

APS/SCM tool for improving Borgo Ricco performances in gas turbine production. The

operations of Florence headquarters were considered as out of scope. The main output of

the first step of the methodology was the list of relevant information requirements, which

follows:

• Simulation of production activities: typical of highly flexible manufacturing systems; it

allows to evaluate the impact of different schedules in term of machines workload and

material availability;

• Integration with suppliers: in terms of both visibility to suppliers (it allows to suppliers

all along the supply chain to align their planning processes to final customer demand

and, in particular, to align capacity with demand as soon as demand changes show up,

thus avoiding the typical delay and bullwhip effects) and visibility on suppliers (it allows

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to evaluate in advance the effects of several purchase alternatives and to communicate

reliable delivery dates to the end customer)

• Sub-suppliers planning and control: typical of companies heavily relying on sub-

suppliers, it gives visibility on third-party production activities (quality control,

production advancement, etc.).

• Alert management: it allows to have real-time information on exceptions, bottlenecks,

capacity constraints violation, thus allowing to promptly adjust plans accordingly.

• Integration with customers: mainly in terms of visibility to customers (Florence),

allowing to increase service level offered to customers, in terms of reliable and

frequently updated (if necessary) delivery dates, immediate order confirmations,

possibility to make variations in the order conditions based on actual production

advancement, etc.

• Available to Promise/Capable to Promise (ATP/CTP): it allows to communicate to the

customer reliable delivery dates based on available materials (raw materials and

components, assemblies and sub-assemblies, finished products) and available

production capacity. This requirement is most relevant in case of complex Bill of

Materials, with many levels and long production cycles – especially with assembly

operations requiring the co-ordination of several independent production flows.

On the basis of the list of information requirements, a set of relevant activities to be

supported by APS/SCM was determined. They are: production programming, suppliers

and sub-suppliers integration and planning, alert management, integration with customers

and stock management, order promising (ATP/CTP).

Once the activities were determined, we moved on to step 2, with the aim of quantifying the

improvement of KPIs due to APS/SCM implementation. This step was successfully carried

out by referring to the activities/performances relationship matrix, which allowed to

identify the set of relevant Borgo Ricco KPIs which can be improved thanks to the

APS/SCM system, and by referring to the relationship network tool (see Figure 2) it was

possible to determine the KPI which are expected to improve due to some improvement in

upstream (first-tier) KPIs. The resulting relevant performances were: timeliness, on-time

delivery, resources saturation, work in process (WIP). Once the relevant KPIs were

identified, the improvement of each of them was quantified by means of structured

approach for KPI improvement evaluation (see Figure 4) or analytically. Some examples are

reported in the following.

Resource saturation reduction 7 rectification machines operating on 3 daily shifts for about 300 days per year:

• Total time = 50,400 hours/year

• Scraps = 330 hours/year

• Break-downs = 500 hours/year

• Problems due to Operators = 1,600 hours/year

• Set-ups = 7,500 hours/year By simulating alternative schedules with lower overall set-up time due to better sequencing

of similar jobs, it was possible to estimate that the improvement of planning activities could

bring to a reduction of set-up times of about 30% å 2,250 hours per year (4.5% of total time).

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485

Gap As-Is:value 80 % deliveries on-time

20 % deliveries Ideal:100% deliveries on-time

Pla

nned

dela

y

(decid

ed

by

Flo

rence)

Deliv

ery

date

anticip

ate

d

Without

notice

Cri

ticalsub-s

upplie

r’s d

ela

y

Accura

cy

of

deliv

ery

date

definitio

n

Shop-f

loor

pro

ble

ms

WIP

Sa

fety

sto

ck

Inte

rnalcost

of

qualit

y

5 65 15 10 4 1 0 0

Transportation planningProduction activities

planningIntegratiopn with customers

And stock managementDelivery date definition

0 0 0 70 50 28 0 0

Planning

Activities

supported

by APS/SCM

system

PERFORMANCE GAP

GAP CAUSE

FACTORS

CAUSE

PERFOR.

GAP REDUCTION [%]

Exte

rnal

causes

9

Figure 4.

The estimated improvement are here summarised:

• timeliness: from 8 month to 7.5 month;

• on-time delivery: from 80% on time delivery to 82% on-time delivery;

• resources saturation: 4.5% of total time freed up for further production activities;

• work in process (WIP): from 220 sets of blades sets to 205 sets of blades. During step 3, the improvements of timeliness and on-time delivery have not been

quantified: their improvement positively impact on the company image of fast and reliable

deliveries. On the contrary, the improvements of resource saturation and WIP have been

quantified as follows:

• Resource saturation:

o Set-up reduction would free up production capacity (2,250 hours/year)

o Average productivity of rectification machines: 10 pcs/hour

o Direct variable costs for rectification process: 1.40 €/piece

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486

o Sub-supplier cost: 10 €/piece

o Annual savings: 193,500 €

• WIP:

o WIP reduction: 14 sets of blades (1 set = about 80 blades)

o Direct variable costs: 850 €/unit

o Average completion degree: 50 %

o Opportunity cost of capital: 10 % per year

o Annual savings on inventory holding costs: 47,600 €

Then, after transient time, an overall annual saving of about 250,000 € is expected.

As for the costs of the APS/SCM system, they were provided by the IT vendor which had

just finished to successfully implement its APS/SCM system in Florence plant. The

APS/SCM system fully satisfied the information requirements identified for Borgo Ricco,

then that vendor appeared a good candidate for APS/SCM implementation in Borgo Ricco.

According to the last step of SNOpAck methodology, the strategic evaluation of the

APS/SCM solution was carried out. The main elements of the analysis are reported in the

following:

• the improvement of on-time delivery and of timeliness strongly contribute to the

improvement of the image of Borgo Ricco and of the company as a whole;

• the APS/SCM system frees up time of planner employees which can be diverted into

improving planning decisions (being more efficient allows to be more effective);

• project risks have been determined (data availability and correctness (g.i.g.o. rule); top

management commitment; employees training) and strong attention should be devoted

to them throughout the project otherwise they could undermine APS/SCM

implementation success;

• the transient time to have the new APS solution up and running was estimated to last

about one year; moreover, given the variability associated with some of the figures

included in the saving estimation, benefits in the following years were protectively set

to just 80% of the expected 250,000 €/year foretold by the procedure in Step 3.

The cash flows have then been recalculated considering the outcome of the risk analysis,

and considering an overly cautious discount rate of 20%/year (worst case). As a result, the

pay-back time for the implementation of the new APS/SCM system is estimated to be as

short as 10 months.

5. Conclusions and future developments

Over the last 5 years, the model has been applied to more than a dozen manufacturing and

service organizations belonging to different sectors. The cases were useful to identify

strengths and weaknesses of the methodology. The objectives of completeness and

objectivity are reached and many of the hypotheses of relations between management

activities and KPIs were confirmed.

Yet, the methodology in its present form shown a major limitation, in that the analysis

considers as given (and, therefore, deterministic) the characteristics of the APS/SCM

solution.

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The output of the methodology is basically the result of a data collection and a data

elaboration phase. While the calculation procedure is really accurate, more could be done

regarding input acquisition. The interviewed managers happened to have difficulties in

imagining the effect of an APS/SCM system on the way their company works: a possible

extension of the methodology includes the development of a set of visual or numerical

examples which will provided to the interviewees during the analysis.

Future research paths also include an extension of the methodology specifically developed

to analyse the operations and the supply chain of service companies.

6. References

Ballantine, J. A., Stray, S., 1999, Information systems and other capital investments:

evaluation practices compared, Logistics information management, Vol.12, No.

1/2, pp. 78-93.

Bowersox, D.J., Closs, D.J., 1996, Logistical Management - The integrated supply chain

process, Mc Graw – Hill, 1996.

Brun, A., Caridi, M., Fahmy Salama, K., Ravelli, I., 2006, Value and Risk assessment of

Supply Chain Management improvement projects, International Journal of

Production Economics, Vol. 99, No. 1-2, pp. 186-201

Fahmy Salama, K., 2002, Value assessment di sistemi informativi SCM: una metodologia di

supporto focalizzata sull’analisi dei benefici, Master of Science Thesis, Politecnico

di Milano.

Farbey, B. and Finkelstein, A., 2000, Evaluation in Software Engineering: ROI, but more than

ROI, Working Paper Series - Dept. of Computer Science University College London

– LSE, URL: http://is.lse.ac.uk/all_wp.htm.

Farbey, B., Land, F. and Targett, D., 1993, How to assess your IT investment: a study of

methods and practice, Butterworth-Heinemann, Oxford.

Fisher, M. L., 1997, What is the right supply chain for your product?, Harvard Business

Review, March-April 1997, pp.105-116.

Frederick R.I., 2000, Mixed Group Validation: a Method to Address the Limitations of

Criterion Group Validation in Research on Malingering Detection, Behavioral

Sciences and the Law, Vol.18, pp.693-718.

Kahnemann D., Slovic P., Tversky A., 1982, Judgement under uncertainty: Heuristics and

Biases, Cambridge University Press.

Orlicky, J., 1975, Material Requirements Planning, McGraw-Hill.

Renkema, T. and Berghout, E., 1997, Methodologies for information systems investment

evaluation at the proposal stage: a comparative review, Information and Software

Technology, Vol. 39, pp. 1-13.

Stadtler, H., Kilger, C., 2000, Supply Chain Management and Advanced Planning, Springer

Verlag, 2000.

Supply Chain Council, 2003, SCOR – Supply Chain Operations Reference – Version 6.0,

URL: http://www.supply-chain.org.

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Turbide, J.D., 1998, APS, Advanced Planning Systems, APS magazine, No.1.

Willcocks, L. and Lester, S., 1993, Evaluating the feasibility of information technology

investments, Research Report RDP93/1, Institute of management, Oxford.

www.intechopen.com

Supply ChainEdited by Vedran Kordic

ISBN 978-3-902613-22-6Hard cover, 568 pagesPublisher I-Tech Education and PublishingPublished online 01, February, 2008Published in print edition February, 2008

InTech EuropeUniversity Campus STeP Ri Slavka Krautzeka 83/A 51000 Rijeka, Croatia Phone: +385 (51) 770 447 Fax: +385 (51) 686 166www.intechopen.com

InTech ChinaUnit 405, Office Block, Hotel Equatorial Shanghai No.65, Yan An Road (West), Shanghai, 200040, China

Phone: +86-21-62489820 Fax: +86-21-62489821

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportationvehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problemset with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technologyadvances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs,and make money. From internal enterprise processes to external business transactions with suppliers,transporters, channels and end-users marks the wide range of challenges researchers have to handle. Theaim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals,prevailing concepts as well as current practical applications.

How to referenceIn order to correctly reference this scholarly work, feel free to copy and paste the following:

Alessandro Brun and Maria Caridi (2008). Assessing Improvement Opportunities and Risks of Supply ChainTransformation Projects, Supply Chain, Vedran Kordic (Ed.), ISBN: 978-3-902613-22-6, InTech, Availablefrom:http://www.intechopen.com/books/supply_chain/assessing_improvement_opportunities_and_risks_of_supply_chain_transformation_projects

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